Corrected Quarterly Results of my Master’s Thesis

Author

Simon Knapstein

This document presents the results of the quarterly event studies conducted for my Master’s Thesis using different specifications of fixed effects (FE).

During my final presentation, we discussed whether I had employed yearly instead of quarterly fixed effects in order to better isolate the effects of my hypotheses from potential seasonal variations.

Upon reviewing my code, I found that I had used time-unit fixed effects, meaning that each quarter was treated as a separate time period rather than as the same quarter across years. Therefore, I additionally experimented with specifications including (i) quarter fixed effects independent of the year and (ii) both quarter and year fixed effects simultaneously.

I now also added plots of averages over time and histograms

Correction

While inspecting the descriptive data I discovered an error in the computation of district-level radiance. I summed all pixel-level radiance values using

extract(light_masked, gadm_vector, 
fun = sum,        
na.rm = TRUE)

This led to biased data toward districts with a larger area because they contain more pixels. As a result, for example, Taliban-controlled districts appeared brighter simply because they were larger and histograms were rather normally distributed.

I now changed the aggregation to

fun = mean

to obtain average radiance per pixel and re-estimated the results.

Event Studies

For 2022 ban:

For 2023 “effective” ban

Quarters 1 and 2 after the Taliban takeover show a positive effect, weakly significant (10%) effect.

model_event_tali <- feols(
  ln_ntl ~ i(event_time, tali_cont_bin_1, ref = -1) | 
    district_fe + 
    time_id,
  data    = ntl_quart_event_tali,
  cluster = ~NAME_1
  
# NAME_1 is province.

model_event_tali <- feols(
  ln_ntl ~ i(event_time, tali_cont_bin_1, ref = -1) | 
    factor(quarter) + 
    year + 
    district_fe,
  data    = ntl_quart_event_tali,
  cluster = ~NAME_1
)

model_event_tali <- feols(
  ln_ntl ~ i(event_time, tali_cont_bin_1, ref = -1) | 
    factor(quarter) + 
    district_fe,
  data    = ntl_quart_event_tali,
  cluster = ~NAME_1
)

event_model_quart_nato <- feols(
  ln_ntl ~ i(event_time, any_military, ref = -1) | 
    GID_2 + 
    time_id,
  data    = did_data_quart_nato,
  cluster = ~GID_1
)
# GID_2 is District code and GID_1 is province code

event_model_year_and_quarter <- feols(
  ln_ntl ~ i(event_time, any_military, ref = -1) | 
    GID_2 + 
    year + 
    factor(quarter),
  data    = did_data_quart_nato,
  cluster = ~ GID_1
)

event_model_quarterFE <- feols(
  ln_ntl ~ i(event_time, any_military, ref = -1) | 
    GID_2 + 
    factor(quarter),
  data    = did_data_quart_nato,
  cluster = ~ GID_1
)

Descriptive Plots

Average Plots over Time

To compare, here the yearly graph:

To compare, here the yearly graph:

Distribution of Taliban-controlled vs. non-controlled districts
Taliban Controlled	Count
0	302
1	26

To compare, here the yearly graph:

Urbanity

To understand whether the (NATO) effects are partly driven by urban–rural differences, I create a measure of “urbanity” using VIIRS Nighttime Lights.

For each district, I take the maximum quarterly radiance value (max_ntl) and place it in the percentile distribution of all districts.

I then define three categories:

major_urban: top 5% brightest districts
mid_urban: 5% - 15% brightest districts
rural: all remaining districts

Descriptive pattern: Urbanity and NATO presence

The table below shows how many observations in each group are NATO-treated.
This is useful to see whether the treatment is spatially concentrated.

Urbanity category	n_total	n_NATO	share NATO
major_urban	504	336	66.7%
mid_urban	840	364	43.3%

Alternative Event studies

H3 NATO

Urbanity as fixed effect

I created an urbanity category as explained before.

event_model_quart_nato_urbanFE <- feols(
  ln_ntl ~ i(event_time, any_military, ref = -1) |
    GID_2 + time_id + urban_category,
  data    = did_data_quart_nato,
  cluster = ~GID_1
)

Further possible models

Exclude Province of Kabul
Province x Quarter FE (regional temporal shocks)
District-specific linear trends